1a. Objectives (from AD-416):
The first objective is to develop and validate multitask in-line real-time inspection technologies for small to large processors that simultaneously detect contaminants and defects of fruits and vegetables. 1a) Evaluate visible/near-infrared reflectance and fluorescence imaging techniques for whole-surface detection of fecal material, damage, defects, and spoilage artifacts on fruits and vegetables. 1b) Identify multispectral wavebands and develop detection algorithms and image segmentation procedures for whole-surface inspection of produce that can be utilized for multitask screening for safety and quality concerns. Integrate and test methods for use in in-line multitask inspection systems. 1c) Develop and evaluate methods to facilitate whole-surface line-scan imaging of fruits and vegetables for in-line inspection. 1d) Develop and evaluate two prototype multitask inspection systems, one for fruits such as apples and tomatoes and a second for leafy green vegetables such as spinach and lettuce. The second objective is to develop and validate portable optical sensing technologies for detecting the presence of chemical and biological contaminants on food products and processing surfaces. 2a) Evaluate fluorescence, reflectance, and Raman spectral and imaging technologies for use in rapid sample analysis to detect fecal contamination, organic residues, bacterial biofilms, and food adulterants. 2b) Develop and validate a portable Raman-based hyperspectral imaging platform that can be used for macro-scale imaging of food samples as large as intact fruits and vegetables. 2c) Develop and validate handheld imaging devices for contamination and sanitation inspection in processing environments. 2d) Develop and validate imaging platform for in-field detection of fecal contamination.

1b. Approach (from AD-416):
The previous project included four patents (pending/issued) for methods and technologies developed: multitask line-scan imaging inspection, macro-scale laser-induced fluorescence imaging, Raman spectral detection of melamine adulteration, and image-based portable handheld sanitation inspection devices. This new project will build upon these previous accomplishments to develop prototype devices for commercialization. Rapid line-scan imaging technologies developed during the previous project cycle will be used to construct prototype whole-surface in-line inspection systems for simultaneously detecting surface contamination and defects using a single camera. This research focuses primarily on fresh fruits and vegetables, such as leafy greens, apples, and tomatoes, and on the detection of defects and of fecal contamination (a recognized source of human pathogens associated with fresh fruits and vegetables). Two prototype whole-surface in-line inspection systems will be developed, one for flat leafy produce such as Romaine lettuce and baby spinach, and a second for round-shaped produce such as apples and tomatoes. These systems will incorporate multitasking capabilities that allow users to select desired inspection criteria, and to optimize wavelengths and thresholds to address changes in produce characteristics on-the-fly. To detect chemical and biological substances of food safety interest, and to address the needs of the fruit and vegetable industries for evaluation or inspection tools for rapid on-site or in situ assessment of food safety risks, portable NIR (1000 to 2200 nm) hyperspectral imaging and Raman hyperspectral macro-scale imaging systems will be developed and validated . These enhanced capabilities will improve the existing toolbox of available imaging technologies for addressing unforeseen biological/chemical contamination problems in a timely manner. To enhance existing survey methods in produce processing plants, a previously developed handheld imaging device for inspecting poultry processing areas will serve as the basis for the development of a similar system for inspecting produce processing surfaces. The handheld inspection devices are intended as assistive tools for human inspectors to use during off-line inspection of processing equipment surfaces. To address the industry-identified need to survey produce fields for fecal contamination, technology to detect feces in produce fields will be developed based on a previously patented laser-induced fluorescence imaging technique. The proposed field imaging platform will assist industry in addressing in-field in situ detection of fecal contamination. As an applied engineering research project, the effective outcome of this work should be commercialization of the technologies developed. Critical to this end is collaboration with industry partners. Thus, this project will continue strategic partnerships with four companies with whom Cooperative Research and Development Agreements (CRADAs) have been established.

3. Progress Report:
Improved prototypes of sample rotation devices and conveyor systems for whole-surface inspection of round fruits and of relatively flat leafy-greens were developed; currently, no such whole-surface online inspection technologies exist for industry use. For the leafy-green inspection system, a second method to flip relatively soft leafy-greens to allow inspection of both surfaces was also developed and tested. These whole-surface image-based inspection methods will allow thorough safety/quality inspection of round fruits and leafy greens on commercial processing lines. An enhanced version of a high-power light-emitting-diode-based lighting system with computer control capabilities and selectivity in either ultra-violet of violet was developed. This new lighting system provides optimize illuminations for in-line produce inspection using fluorescence-based imaging technologies. These ARS technologies will allow thorough online safety inspection of fruits and leafy-greens and address food safety hazards related to surface contamination and defects.
A new line-scan based Raman chemical imaging system was designed and developed. Preliminary experiments suggested that the new line-scan system is capable of acquiring spatially-resolved Raman spectra three orders of magnitude faster than the previous version. The improvement in scanning speed allows the use of the technique as a routine scanning tool in food industries.
For sanitation inspection in food processing environments, a recently developed fluorescence-based hyperspectral imaging system was tested at two commercial fresh-cut produce processing plants to examine the efficacy of routine sanitation and cleaning procedures. Visualization of the contaminants using the imaging system allowed normal cleaning and sanitation procedures to be revised to better utilize cleaning efforts. Handheld commercial prototype versions were built and transferred to a commercial partner under an MTA to allow reverse engineering for development of commercial units. During some visits to the commercial facilities, micro-biological samples were taken from selected surfaces. Of particular interest was the ability of recovered bacteria to form biofilms. In collaboration with other EMFSL scientists, a number of biofilm-forming bacteria were identified and characterized, and their ability to form viable dual-species biofilms with pathogenic strains evaluated.
To enable measurement of fluorescence responses in the presence of ambient solar radiation, a novel laser-induced fluorescence imaging system was constructed and tested. Subsequently, a spectral adapter was added to allow hyperspectral image acquisition. The final component needed to complete the development of an integrated imaging system was a method for converting a 4 mm Gaussian laser pulse into a line illumination source. This year, tests demonstrated that Powell lenses can be used to expand the laser beam into a homogenous line illumination source. The integrated system is now being tested by imaging apples and spinach leaves artificially contaminated with nano-gram quantities of bovine manure.

4. Accomplishments
1. Handheld imaging devices for contamination and sanitation inspection of food contact surfaces. For sanitation inspection in food processing environments, we recently designed and developed inexpensive fluorescence-based handheld imaging devices with wi-fi capabilities to display live inspection images on smartphone or tablet devices. The aim is to provide the imaging devices as assistive tools that can be used by human inspectors performing visual sanitation inspection of food processing/handling equipment surfaces. Fluorescence imaging techniques with high power LED illumination and multispectral emission bands are used to detect the presence of fecal contamination, organic residues, and bacterial biofilms on surfaces under ordinary conditions of ambient light. The devices can provide an objective means to assess the effectiveness of sanitation procedures and can help processors minimize food safety risks or determine potential problem areas within a processing environment. A U.S. patent (“Hand-held Inspection Tool and Method”) was granted in November 2012 and a licensing agreement for the technology is in its final negotiation stage. Under an MTA, ARS prototypes were provided to a commercial partner to help develop a commercial version.

2. Point-scan Raman imaging-based detection of food contaminants. Incidents in recent years of profit-driven adulteration of milk and wheat ingredients subsequently used to make dairy products and pet foods have highlighted the need for non-destructive methods to screen food ingredients for contaminants that can pose significant food safety hazards. A Raman chemical imaging system and method were developed for detecting multiple adulterants in dry skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into petri dishes containing milk powder, at concentrations between from 0.1% to 5.0%. Using the 785-nm laser system, a 25 mm × 25 mm square area of each mixture was imaged. Spectral image processing methods were developed to remove interference from background fluorescence, and to create Raman chemical images visualizing the distribution of the different adulterants in the milk powder using unique Raman peaks of the adulterants. A correlation was found between adulterant concentration and the number of adulterant pixels identified in the images, demonstrating the potential of this method for quantitative analysis of adulterants in milk powder. A U.S. patent 8,467,052 (“System and Methods for Detecting Contaminants in a Sample”) was granted in May 2013.

3. Spectral imaging technologies for safety inspection of agricultural products. The ARS Sensing technology team in Beltsville, MD, has been at the forefront of developing cutting-edge nondestructive food safety evaluation technologies for food processing industry implementations. For meat processing industries, an image-based method (U.S. Patent #7,460,227) to rapidly detect exposed bone fragments was developed to mitigate potential bone-fragment contamination in processed meat products. Current USDA regulations prohibit the sale of systemically diseased chickens for human consumption; these birds are detected by human inspectors for removal from poultry processing lines. A line-scan spectral imaging system was developed for automated wholesomeness inspection of freshly slaughtered chickens (US Patent # 8,126,123). In order to address multiple safety and quality inspection requirements for the fresh produce industry, a multitask on-line inspection method (U.S. Patent # 7,787,111) for detection of contaminants and defects on fruits and vegetables was developed. The optical technology allows simultaneous acquisition of fluorescence and reflectance images to detect fecal contaminants and defects on the surfaces of produce. A partner requested licensing for the portfolio of above patented ARS sensing technologies for commercial implementations. The Office of Technology Transfer, ARS, is negotiating with the commercial partner to finalize the licensing agreement. These ARS sensing technologies will help US food industries to improve processing efficiency and reduce food safety risks while maintaining their global competitiveness.

4. Near-infrared hyperspectral imaging for rapid detection of food adulterants. Melamine is a nitrogen-rich chemical that is commonly found in the form of white crystals, and in many reported cases was found intentionally added to food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks to boost the perceived protein content. The resulting cases of illness and death have raised concerns about food safety and the tools available to screen foods and food ingredients for harmful adulterants. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely investigated for a variety of food quality and safety evaluations. In this study, a near-infrared hyperspectral imaging technique was used for rapid identification/detection of melamine particles in milk powders. We demonstrated that spectral similarity analyses of hyperspectral images could successfully identify melamine particles in a range of melamine-milk mixtures with melamine concentrations as low as 0.02% (200 ppm). This spectral imaging method can also be applied to other chemicals or multi-chemicals for adulterant detection in milk powders.

5. Portable hyperspectral imaging system for cleaning and sanitation inspection in produce processing facilities. Effective cleaning and sanitation procedures are critical for reducing risks of foodborne illness. However, it is difficult to validate the effectiveness of such procedures. On a routine basis, effectiveness is currently judged by inspection using the naked eye. Fluorescence imaging has been proposed as a more sensitive method to detect food residues that might remain after cleaning and sanitation procedures. A portable hyperspectral imaging system was developed that allows large surface areas to be surveyed in real time. The system was tested in multiple visits to two commercial fresh-cut processing plants. The imaging system was able to detect residues following cleaning and sanitation that were not visible or were barely visible to the naked eye. Detection of these residues allowed normal cleaning and sanitation procedures to be revised to better utilize cleaning efforts. The net result was cleaner surfaces at no additional cost in manpower.

6. Multispectral fluorescence imaging for detection of frass on tomatoes. Preliminary research by ARS scientists has found that frass (the excrement of insects) is one possible vector for the transmission of pathogens such as E. coli and Salmonella to fresh produce. Therefore, rapid detection on postharvest processing lines for frass contamination of fresh produce could help to prevent or minimize the potential safety risks of fresh produce. A simple multispectral fluorescence image processing algorithm was developed for the detection of tomato hornworm frass contamination on mature red tomatoes. Contamination spots were applied to the tomatoes using frass dilutions prepared at four different concentrations. The results showed over 99% successful detection of spots created using 0.2 and 0.1 g/ml frass dilutions, while spots created using lower concentrations at 0.05 and 0.02 g/ml were more difficult to detect and may require more complex image analysis methods to achieve similar accuracies. The method could be adapted for screening other agricultural products.

7. Spectral imaging techniques for detection of cracked tomatoes. Tomatoes are one of the most popular produce items in the United States, behind only potatoes, lettuce, and onions. Cracking is a serious problem that reduces the quality of tomatoes and increases the cost of tomato production. Open cracks can potentially allow internalization of contaminants, including pathogens. Scientists at ARS have been developing spectral imaging-based inspection techniques for produce safety and quality. In this study, a simple reflectance-based spectral imaging algorithm was developed to detect defect cracks on mature tomatoes. The results demonstrated that the image algorithm could differentiate normal tomatoes from defect cracked tomatoes with high accuracy. This method could be implemented on automated processing lines for rapid and accurate detection of cracked tomatoes.